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Enhancing Industrial Internet of Things Network Security: An Extensive Investigation on Sophisticated Feature Selection and Reduction Methods Using Machine Learning Algorithms

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SSRN 2024年

作者： Houkan, Ahmad Sahoo, Ashwin Kumar Gochhayat, Sarada Prasad Dept. Of Electrical Engineering C. V. Raman Global University Bhubaneswar India Dept. Of Computer Science Engineering Indian Institute of Technology–Jammu Kashmir Jammu India

The proliferation of Internet of Things devices has raised serious cybersecurity issues, leading to the need for robust intrusion detection systems, as well as feature reduction and selection to support anomaly detection in industrial Internet of Things networks. In a comparative study, experiments were conducted to compare the efficiency of feature selection using Minimum Redundancy Maximum Relevance with feature reduction employing Principal Component Analysis. Through systematic evaluations, three machine learning algorithms – Decision Trees, k-nearest-neighbors, and Gaussian Support Vector Machine– are scrutinized across binary and multi-class classification scenarios, accommodating varying feature *** underscore Minimum Redundancy Maximum Relevance and Principal Component Analysis prowess in discerning feature significance. With a mere 12 features, Minimum Redundancy Maximum Relevance facilitates high accuracy rates, expedites training processes, yields compact models, and demonstrates comparable or enhanced performance vis-a-vis prior investigations. Notably, the Decision Tree classifier excels in binary classification achieving a remarkable 99.9%, while the k-nearest-neighbors algorithm achieves a remarkable 99% accuracy in multi-class tasks. These findings underscore the efficiency of judicious feature selection in fortifying industrial Internet of Things systems through machine learning, while striking a balance between classification efficiency, training overheads, and model intricacy, tailored to specific application demands. © 2024, The Authors. All rights reserved.

关键词： Internet of things

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Benefits of Internet of Things (IoT) Applications in Health care - An Overview 1

Benefits of Internet of Things (IoT) Applications in Health ...

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1st International Conference on Computational Intelligence, Communication technology and Networking, CICTN 2023

作者： Salama, Ramiz Al-Turjman, Fadi Chaudhary, Preeti Yadav, Satya Prakash Ai and Robotics Institute Research Center for Ai and IoT Near East University Nicosia Department of Computer Engineering Mersin10 Turkey Ai and Robotics Institute Near East University Nicosia Artificial Intelligence Engineering Dept. Mersin10 Turkey University of Kyrenia Research Center for Ai and IoT Faculty OfEngineering Kyrenia Mersin10 Turkey Graphic Era Hill University Computer Science and Engineering Dehradun India Department of Computer Science and Engineering Greater Noida201306 India

ISBN: (纸本)9798350338027

In a nutshell, the IoT refers to the idea of integrating high-tech technology in the form of "smart"gadgets and sensors into a single setting to gather data. There are still numerous areas in which machines cannot compete favorably with people, but data collection and analysis are undeniably two of their strongest points. The Internet of Things makes it possible to fully or partially automation data, it involves related activities, which is obviously very important for the healthcare industry. What exactly is the Internet connected medical things, and how exactly might it make a significant impact on the sector? By having to apply the concept of the internet of things (IoT) to the field of medicine, several significant advancements have become possible, including the ability to automatically control the thermostat of containers while transmitting inoculations and antibiotics, the accurate tracking of the ailments of a lot of illnesses without a need for additional meetings with a general practitioner, the correct and most useful use of meds, and a great deal more. All of this is realizable because of the capability of MedTech gadgets to carry out continuous monitoring system. The potential applications of internet of things technology in healthcare are very interesting. Let's review the ways in which this technology. © 2023 IEEE.

关键词： Medical applications

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A hybrid kernel support vector machine for predicting protein structure

A hybrid kernel support vector machine for predicting protei...

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2021 International Conference on Automation, Control and Mechatronics for Industry 4.0, ACMI 2021

作者： Khan, Ishrat Abdul Fattah, H M Shill, Pintu Chandra Khulna University of Engineering Technology Dept. of Computer Science and Engineering Khulna Bangladesh

ISBN: (纸本)9781665438438

Protein structure prediction is very important to gaining insights for diseases diagnostics, medical treatment, and human biology. Here, we present a technique to predict the protein structure from its amino acid composition (AAC) using the hybrid kernel support vector machine. First and foremost, data preprocessing is done to reduce the irrelevant and noisy data. Then, a multiple class feature selection is used to reduce data dimensionality. Then, to improve the accuracy of support vector machine classifier, a hybrid kernel function is generated by combining the radial basis function (RBF) and polynomial kernel. Lastly, training the hybrid kernel support vector machine using the preprocessed training dataset. We used 25PDB dataset, where the sequence similarity is below 40%. From the result analysis, it is evident that for high dimensional datasets, the proposed method performs best with respect to accuracy. Moreover, our proposed method outperforms many of the existing methods. © 2021 IEEE.

关键词： Support vector machines

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Proposal and Demonstration of Power Conversion-Chip/Amplifier Integrated Antenna 50

Proposal and Demonstration of Power Conversion-Chip/Amplifie...

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50th European Microwave Conference, EuMC 2020

作者： Hara, Shinji Suzuki, Asako Hirayama, Hiroshi Nagoya University IMaSS Japan Nagoya Institute of Technology Dept. of Computer Science and Engineering Japan

ISBN: (纸本)9782874870590

The concept of a power conversion chip integrated antenna (PIA) is proposed for microwave power transmission applications, eliminating the matching circuit between the power conversion chip and antenna to minimise the loss between them. A final stage power amplifier, which is one of the key components of the PIA, was designed and demonstrated. The fabricated 2.4-GHz 33-W GaN HEMT power amplifier demonstrated a drain efficiency of 82.3% and a power added efficiency of 78.8%. A power amplifier integrated antenna, i.e., quasi-PIA, was also demonstrated. © 2021 EuMA.

关键词： III-V semiconductors

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Feature Distraction Based Backdoor Defense for Federated Trained Intrusion Detection System

Feature Distraction Based Backdoor Defense for Federated Tra...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Chen, Yu-Wen Ke, Bo-Hsu Wang, Yen-Xin Lin, Shih-Heng Tsai, Ming-Han Chen, Bo-Zhong Kuo, Jian-Jhih Hwang, Ren-Hung New York City College of Technology BrooklynNY United States National Yang-Ming Chiao Tung University College of Artificial Intelligence Taiwan National Chung Cheng University Dept. of Computer Science and Information Engineering Chiayi Taiwan

ISBN: (纸本)9798350351255

Convolutional Neural Network (CNN)-based Intrusion Detection System (IDS) incorporated with Federated Learning (FL) facilitates collaborative model training to secure user privacy and enhance data diversity, emerging as a promising solution to defend malicious flows. Yet, FL is vulnerable to trigger backdoor attacks. Existing methods have limited ability on IDS with FL for detecting or repairing a converged attacked model as they either require access to the training process or are dedicated to specific applications. To tackle the limitations, we propose a novel Features Distraction Defense Framework (FDDF) that does not interfere with the model training and requires only a few additional model inferences to protect users from the aforesaid attack. The experiment results show that FDDF can recover the accuracy effectively and preserve the accuracy of normal models. © 2024 IEEE.

关键词： Network intrusion

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Navigating Green Transportation in Urban Agglomerations: A Refined Approach for Carbon Emissions Assessment 24

Navigating Green Transportation in Urban Agglomerations: A R...

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24th COTA International Conference of Transportation Professionals: Resilient, Intelligent, Connected, and Lowcarbon Multimodal Transportation, CICTP 2024

作者： Chen, Boyou Wang, Yuanqing Abolarin, Emmanuel Zhang, Shuangyan Liu, Xihao Guo, Hanlin Ren, Baolong Dept. of Built Environment Bartlett School of Planning Univ. College London London United Kingdom Dept. of Transportation Engineering College of Transportation Engineering Chang’an Univ. Xi’an China Dept. of Industrial and Manufacturing Systems Engineering College of Engineering and Computer Science Univ. of Michigan–Dearborn Dearborn United States School of Civil and Transportation Engineering Beijing Univ. of Civil Engineering and Architecture Beijing China Sino-French Engineer School Beihang Univ. Beijing China Dept. of Instrument Science and Technology College of Mechanical Engineering Nanjing Univ. of Science and Technology Nanjing China Sino-Danish College Univ. of Chinese Academy of Sciences Beijing China

ISBN: (数字)9780784485484

ISBN: (纸本)9780784485484

Rapid urbanization has expanded urban agglomerations, escalating carbon emissions from increased transportation needs. These agglomerations have distinct multi-modal transportation systems. Analyzing their carbon footprints and eco-friendly transportation evolution is essential for sustainability. However, research in this domain is limited. This research refines emissions estimation methods, introducing models tailored for urban agglomerations. An evaluation framework for green transportation detailing a computational method for future policies is proposed. The research highlights primary transportation structures and energy consumption. Using a "bottom-up" approach, carbon estimation models for inter- and intra-city transportation are drafted. Assessment indicators are redefined by merging the DPSIR model with urban metrics, forming five main modules with 19 indicators. Using the Regression Random Forest method and focusing on the Yangtze River Delta in China, the link between annual emissions and these indicators is examined, providing a theoretical analysis approach for general cases. © ASCE.

关键词： Carbon sequestration

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Optimal Resource Allocation in Dynamic Fading Channels via Meta-Learning Techniques

Optimal Resource Allocation in Dynamic Fading Channels via M...

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Advancements and Key Challenges in Green Energy and Computing (AKGEC), International Conference on

作者： Deepak Upadhyay Kunj Bihari Sharma Mridul Gupta Abhay Upadhyay Nookala Venu Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era deemed to be University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era deemed to be University Dehradun India Centre for Internet of Things Madhav Institute of Technology & Science Deemed University Gwalior India

ISBN: (数字)9798350390179

ISBN: (纸本)9798350390186

In this paper, we address the optimally dynamical resource allocation problem for fading channels utilizing meta-learning methods with an application to improve efficiency and performance in wireless communication systems. Extensive experiments are conducted and our meta-learning model has demonstrated the ability to adapt itself dynamically in various channel conditions, leading to improvement of Signal-to-Noise Ratio (SNR), reduction of Bit Error Rate (BER) and enhancement on throughput when compared with traditional methods. Visualizations like performance analysis comparisons, heatmaps or 3D plots are used to show how the model can be leverage as effectively manager of resources where they are and when they suit best. The performance improvements demonstrated here are statistically significant testifying for the scalability of our work. The findings of this study demonstrate the major benefits that can be achieved by meta-learning to overcome immanent issues in fast-changing wireless conditions and its potential to propel future communication technologies forward.

关键词： Metalearning Fading channels Wireless communication Heating systems Adaptation models Visualization Bit error rate Throughput Resource management Signal to noise ratio

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An Efficient Authentication and Key Agreement for Vehicular communications

An Efficient Authentication and Key Agreement for Vehicular ...

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Interdisciplinary Approaches in technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Arun Sekar Rajasekaran A. Obulesu Azees Maria T Sivakumar S. Vimalnath Dept. of ECE SR University Warangal Telangana India Dept of Information Technology Vidya Jyoti Institute of Technology (A) Hyderabad India School of Computer Science and Engineering VIT-AP University Amaravati India Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore India Dept. of ECE M.Kumarasamy College of Engineering Karur India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Vehicular Ad Hoc Networks (VANETs) are crucial in improving transportation infrastructure by supporting vehicle to infrastructure (V2I) communication. Nevertheless, maintaining secure and efficient interoperability in such environments has always been a big push because of high mobility, and the vulnerability to cyber threats. This paper puts forward a novel authentication and key agreement protocol to be used in V2I communication settings of VANETs taking into consideration security and efficiency factors. The work in the proposal uses a lightweight cryptographic solution to maintain authentication of vehicles and the infrastructure without incurring much computational and communication load. In general, it is possible to use elliptic curve cryptography (ECC), and hash functions to protect the exchange from basic threats like impersonation, replay and so on. In addition, the proposed protocol includes a session key agreement mechanism that builds the secure communication channels in a dynamic way and provides confidentiality and integrity for the data during the exchange of messages. The simulation results show that the proposed scheme has better performance and it has lower computational overhead and communication overhead than the other related protocols.

关键词： Technological innovation Protocols Vehicle-to-infrastructure Simulation Authentication Vehicular ad hoc networks Transportation Elliptic curve cryptography Cryptography Vehicle dynamics

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Tolerating Soft Errors using Enhanced Matrix Code

Tolerating Soft Errors using Enhanced Matrix Code

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2021 International Conference on Information and Communication technology for Sustainable Development, ICICT4SD 2021

作者： Khan, Md. Mizanur Rahman Sadi, Muhammad Sheikh Al-Zihadi, S.M. Saha, Jyotirmoy Khulna University of Engineering and Technology Dept. of Computer Science and Engineering Khulna Bangladesh

ISBN: (纸本)9781665414609

Due to technological advancements, semiconductor chips are being more prone to soft errors (SEs). As SEs are vastly concerned for circuit reliability, they are to be detected and corrected duly reducing time/space tradeoffs. Among enormous existing approaches, error detection and correction code based on Matrix Code (MC) has a great popularity because of its simplified calculations. This paper proposes a novel method titled as Enhanced Matrix Code (EMC), which boosts up the capability of existing matrix code regarding correction rate. The proposed method depends on forming a new matrix with the help of Hamming codes and Parity codes followed by tricky employment of matrix code, so that detection and correction of multiple errors are conquered. However, it increases a bit time overhead to achieve this improvement. The technique is experimented utilizing several fault injection assessments. Experimental outcomes show how it outperforms existing approaches. © 2021 IEEE.

关键词： Error correction

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Automating Financial Management: An Exploration of Automatic Expense Tracking Systems

Automating Financial Management: An Exploration of Automatic...

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2024 IEEE International BIT Conference, BITCON 2024

作者： Lokesh Naik, S.K. Ravi Kumar, G. Kiran, Ajmeera Ganesh, D. Naresh, K. Madgi, Manohar MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India College of Engineering & Technology CMR CSE Dept. Telangana Hyderabad India Vardhaman College Of Engineering Department Of CSE Hyderabad India TKR College of Engineering & Technology Department of Computer Science and Engineering Telangana Hyderabad500097 India KLE Technological University School of Computer Science and Engineering Karnataka Hubballi580031 India

ISBN: (纸本)9798331518394

This research examines the impact of automatic expense tracking systems on personal financial management. These systems utilize machine learning and advanced categorization algorithms to enhance financial awareness, streamline budgeting, and reduce the manual effort required for tracking expenses. The study demonstrates that machine learning models outperform traditional rule-based approaches in accurately categorizing complex transactions. Users report significant improvements in financial oversight and time savings, with high satisfaction rates particularly among tech-savvy individuals who appreciate the integration of these systems with broader financial tools such as budgeting and investment applications. However, privacy and data security concerns are prominent, underscoring the need for robust protection measures to ensure user trust. The research also highlights positive behavioral changes, including better adherence to budgets and enhanced long-term financial planning due to goal-setting features. Despite the challenges, the potential of automatic expense tracking systems to revolutionize personal financial management is substantial. Future advancements should focus on improving accuracy, enhancing user customization, and ensuring comprehensive integration with other financial management tools. This paper concludes that while these systems offer significant benefits, addressing privacy and customization concerns is crucial for their widespread adoption and effectiveness. © 2024 IEEE.

关键词： Budget control

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